Suchergebnisse

AbstractWith the rapid growth of urbanization and industrialization, the strain on resources and the environment has intensified, resulting in challenges such as soil erosion and biodiversity loss. To address these issues, the development of a circular economy and eco‐cities has become crucial. This article proposes an ecosystem assessment model based on the backpropagation neural network (BPNN) that incorporates environmental carrying capacity and energy regeneration capacity. The model aims to evaluate the ecological service value of wetlands in the Changjiang River basin. The results demonstrate that the proposed model outperforms other comparison methods, exhibiting a high level of accuracy. Furthermore, the simulation outcomes indicate that enhancing eco‐efficiency can enhance the environmental carrying capacity and energy regeneration capacity of the ecosystem, ultimately leading to an overall improvement in ecosystem value.

AbstractVarious policies have been implemented to encourage the development of family farms and cooperatives in the past decade in China. New technology adoption is a crucial part in promoting the performance of family farms. However, empirical evidence on whether and how cooperatives would facilitate family farms to adopt new technologies remains unclear. To address the gap, this paper examines the impact of family farms' cooperative membership on new technology adoption (i.e., new varieties, new machinery, new fertilizers, new pesticides, new pest control techniques, new production methods and new management methods). Using novel survey data from 848 family farms in China, and employing both propensity score matching approach and endogenous switching probit model, we find that for family farms, cooperative membership has positive and significant impacts on new technology adoption. When looking into different types of technology, we find that the impacts are larger on the adoption of new methods than new techniques. Moreover, the impacts are larger for family farms with lower operation income and smaller size. The findings provide important insights for policymakers tasked with promoting agricultural technology adoption.